Opleiding: Data Analysis with R
In the course Data Analysis with R you will learn programming in the R language and how you can use R for data analysis and visualization.R Intro
The course Data Analysis with R starts with the installation of R and the R Studio development environment. The basic syntax of R and the installation of R packages are also discussed.
Plotting in R
Next you will learn how you can quickly gain insight into the data with the ggplot2 package by means of plots. The different plot types, themes and layouts are discussed as well.
Transformations
Then it is time for the dplyr package with which common data transformation problems such as filtering, sorting, summation and grouping can be solved.
Data Cleaning
Presenting data with the rmarkdown package is also covered. As well as tidying raw data with the tidyr package, where columns become variables and rows become observations.
Date and Times
Time series occur in many data sets. The processing of these time series is addressed with the lubridate package that has many useful functions for processing dates and time.
Data Import
Part of the course program is also the import of data from CSV files and file formats from other statistical packages such as SPSS or SAS. Reading from and writing to databases is also treated.
Statistical Analysis
Finally the course Data Analysis with R deals with statistical analysis models such as linear and non-linear models, variable transformations and regressions. All this is supported with many practical examples and can also be applied to cases that are brought along by the students.
Audience Course Data Analysis with R
The course Data Analysis with R is intended for Big Data analysts and scientists who want to use R to analyze their data and to make static analyzes.
Prerequisites Data Analysis with R
Experience with programming is beneficial to good understanding but is not required.
Realization Training Data Analysis with R
The theory is discussed on the basis of presentations and examples. The concepts are explained with demos. Then there is time ample to practice with it yourself. R-Studio is used as a development environment. Course times are from 9:30 am to 16:30 pm
Certification Course Data Analysis with R
After successful completion of the course the participants receive an official certificate R Programming.
Modules
Module 1 : Intro R
- Overview of R
- History of R
- Installing R
- The R Community
- R Development
- R Studio
- R Console
- R Style
- Using R Packages
- Cheatsheets
- R Syntax
- R Objects
Module 2 : Graphics and Plots
- ggplot2
- Graphics Devices and Colors
- High-Level Graphics Functions
- Low-Level Graphics Functions
- Graphical Parameters
- Controlling the Layout
- Changing Plot Types
- Quick Plots and Basic Control
- Aesthetics
- Changing Plot Types
- Labels
- Themes and Layout
Module 3 : Transformations
- dplyr
- R Functions
- Functions for Numeric Data
- Scoping Rules
- mutate
- arrange
- group by
- summarize
- select
- filter
- joining
- dataframe
Module 4 : Presentation
- rmarkdown
- Reproducible research
- Reporting
- Sharing results
- Repetitive Tasks
- Family of apply Functions
- apply Function
- lapply Function
- sapply Function
- tapply Function
Module 5 : Data Cleaning
- tidyr
- spread
- gather
- seperate
- unite
- Logical Data
- Missing Data
- Character Data
- Duplicate Values
- NA’s
Module 6 : Date Times
- Time and Date Variables
- lubridate
- Setting a datetime
- Getting values from a datetime
- strftime Command
- strptime Command
- as.Date function
- Datetimes Calculations
- difftime Command
- Time Series Analysis
Module 7 : Data Import
- R Datasets
- Data.Frames
- Importing CSV Files
- Import from Text Files
- Import from Excel
- Import from Spss or SAS
- Connecting to a database
- Connecting to a cluster
- Databases and ODBC
- dbplyr
Module 8 : Linear Models
- What is a model?
- Statistical Models in R
- How to evaluate a model?
- How to use a model?
- Simple Linear Models
- logistic regression
- linear regression
- R squared
- p values
- confidence intervals
Module 8 : Non-Linear Models
- Decision Trees
- random forest
- boosting
- overfitting
- Optional material :
- Interactive dashboards with Shiny
- Web Scraping
- Writing packages
- Spark
- Functional programming
